Next Article in Journal
Regional Disparity and Patients Mobility: Benefits and Spillover Effects of the Spatial Network Structure of the Health Services in China
Previous Article in Journal
Family Context Assessment in Middle Childhood: A Tool Supporting Social, Educational, and Public Health Interventions
Article

Validity and Psychometric Properties of the Internet Gaming Disorder Scale in Three Large Independent Samples of Children and Adolescents

German Center for Addiction Research in Childhood and Adolescence (DZSKJ), University Medical Center Hamburg-Eppendorf (UKE), Martinistrasse 52, D-20246 Hamburg, Germany
*
Author to whom correspondence should be addressed.
Academic Editor: Paul B. Tchounwou
Int. J. Environ. Res. Public Health 2021, 18(3), 1095; https://doi.org/10.3390/ijerph18031095
Received: 11 December 2020 / Revised: 21 January 2021 / Accepted: 22 January 2021 / Published: 26 January 2021

Abstract

Background: Problematic gaming has become a major health issue in children and adolescents resulting in the need for targeted valid and reliable screening instruments. This study aimed to explore the psychometric properties and criterion validity of the widely used 9-item Internet Gaming Disorder Scale (IGDS) in young gamers. Methods: Three independent samples were drawn from socio-demographically representative cross-sectional telephone surveys collected in the years 2016 (N = 762), 2017 (N = 777), and 2018 (N = 784) and analyzed separately. Results: The IGDS revealed psychometric properties suitable for screening in large samples. Cronbach’s alpha was 0.563, 0.724, and 0.778. The unidimensionality assumption was challenged. At-risk and pathological gamers compared to normal gamers reported longer digital media use and more emotional symptoms and hyperactivity/inattention with clinical relevance to medium effect sizes. The comparison of at-risk and pathological gamers indicated a partial distinction between the two problematic gaming groups. Conclusions: The IGDS could be shown to be an overall suitable and valid tool to identify pathological gamers in childhood and adolescence according to the DSM-5 criteria on a population level. However, the polythetic structure limits comparability with the recent ICD-11 criteria. At-risk gamers appeared as a heterogeneous group warranting more research.
Keywords: gaming disorder; assessment; Internet Gaming Disorder Scale; psychometrics; validity; adolescents gaming disorder; assessment; Internet Gaming Disorder Scale; psychometrics; validity; adolescents

1. Introduction

The use of digital games has significantly increased during recent years and especially the last months [1]. Among smartphone apps, digital games are the most popular with increasing revenue [2,3]. Children and adolescents are particularly prone to these media since a lot of games are designed to address their special interests and support strong attachment by graphics, stories, and the application of intermittent reward system techniques [4,5]. Their frequency and daily time spent with games on electronic devices significantly increased under the first lockdown measures of the COVID-19 pandemic [6]. While the neural reward system of children and adolescents is fully developed, their cognitive control mechanisms are still immature [7]. Together with the higher exposure to digital games, this makes them especially vulnerable to develop at-risk or pathological gaming behavior [8,9]. Pathological gaming is estimated by prevalence rates between 1.2% to 5.9% in European, Asian, and Australian adolescents affecting sleep and academic achievement as well as family relationships [10].
Hence, pathological gaming has become a major concern for health care professionals during the last years. This aspect has been considered by adding the Internet gaming disorder (IGD) as a “condition warranting more clinical research and experience” in the Diagnostic and Statistical Manual of Mental Disorders 5 (DSM-5) of the American Psychiatric Association (APA) in 2013 [11]. According to a systematic review focusing on children and adolescents “multiple, often interacting personal, familial, and environmental risk factors and comorbid conditions will contribute towards developing and maintaining” an IGD [12]. Pathological gamers show an average gaming time of 30 h per week [13,14]. More precisely, 9- to 19-year-old Italian children and adolescents screened with an IGD stated 22 to 27 gaming hours weekly [15]. 12- to 18-year-old Spanish males diagnosed with an IGD reported a weekly gaming time of 48 h before treatment [16].
According to the DSM-5, an IGD is assumed if at least five out of nine defined criteria have been met in the past 12 months (Table A1 in the Appendix A). Moreover, research findings and clinical observations lead to the inclusion of the diagnoses Gaming Disorder (GD) and Hazardous Gaming (HG) in the recent 11th revision of the International Classification of Diseases (ICD-11) of the World Health Organization (WHO) [17]. Given the relevance of this relatively new disorder, research and practitioners are calling for functional assessment measures, especially for screening in children and adolescents.
Available DSM-5 based assessment scales vary regarding their completeness of contents and the target age groups [10]. Self-rating instruments that have been used in young samples include e.g., the 10-item Internet Gaming Disorder Test (IGDT-10) by Király et al. [18], the 9-item Internet Gaming Disorder Scale (IGDS) by Lemmens et al. [19], the 9-item short form of the Internet Gaming Disorder Scale (IGDS9-SF) by Pontes and Griffiths [20], and the Video-Gaming Scale for Children (VGS-C) by Donati et al. [21]. The latter is an adaption of the Video-Gaming Scale for Adolescents (VGS-A) developed by the authors to specifically address the needs of children [22]. In their large review King et al. [23] identified 32 IGD assessment tools available in English that were employed in 320 studies and differed regarding “(1) conceptual and practical considerations; (2) alignment with DSM- 5 and ICD-11 criteria; (3) type and quantity of studies and samples; and (4) psychometric properties”. No single instrument was found to be clearly superior but five scales showed the best psychometric properties. One of these was the Lemmens’ IGDS, studied in 42 refereed publications from January 2010 to September 2020 (https://pubmed.ncbi.nlm.nih.gov/). It appears to be an IGD measure that is both complete regarding the coverage of DSM-5 criteria and appropriate for various age ranges as it was validated in a sample of 13 to 40 year old participants. Overall, this makes it an authorized candidate for a validation study in a target group of children and adolescents.
The IGDS uses a binary question format (yes/no) with a possible maximum sum score of 9 for all “yes” answers and higher scores indicating more problematic gaming behavior. Lemmens et al. [19] report unidimensionality according to the fit indices of a confirmatory factor analysis (CFA) and an excellent consistency with a Cronbach’s alpha of 0.93. Results of a latent-class analysis (LCA) allowed separation of light or normal gamers (scoring ≤2), heavy or at-risk gamers (scoring 3–4), and disordered [pathological] gamers (scoring ≥5). Convergent validity is supported by small to medium correlations of the IGDS sum score with psychological constructs like aggressiveness and antisocial behavior, anger control and hyperactivity, loneliness and emotional distress, low self-esteem, life satisfaction, and prosocial behavior [19,24,25]. The time spent with games could be identified as a predictor for an IGD development one year later [25]. Moreover, a problematic social media use could be shown to be significantly associated with an IGD [26].
However, the results concerning unidimensionality and good internal consistency could not be entirely replicated. In an analysis using item response theory, the criteria ‘escape’, ‘deception’, and ‘continuation’ did not contribute to diagnostic accuracy [24]. In a recent Delphi study, 29 international experts with clinical and/or research experience on IGD rated the DSM-5 criteria ‘escape’ and ‘tolerance’ as inadequate to distinguish between problematic and normal gaming [27]. According to the results of a factorial analysis on data based on the Compulsive Internet Use Scale (CIUS) [28], the item on the criterion ‘escape’ (IGDS item 8) had no association to the other items in a student sample compared to two adult samples [29].
A further validation of these aspects in different samples from a developmental science perspective is urgently required. To date, current research on IGD assessment neglects possible specifics of different age and sex groups. It is common to include children, adolescents, and adults into large-sized psychometric studies without age- or sex-related analyses and with males being usually oversampled. Even in the aforementioned informative and comprehensive systematic review on IGD assessment tools by King et al. [23], age and sex were no evaluation criteria. Furthermore, there is sparse knowledge concerning at-risk gamers. Can they be separated from normal and pathological gamers as e.g., suggested by Milani et al. [15]? Hence, the present study aimed to investigate the psychometric properties of the IGDS in children and adolescents considering different age and sex groups. The convergent validity was explored by comparing normal with at-risk and pathological gamers in terms of time spent with digital media as well as co-occurring emotional and behavioral problems.

2. Methods

2.1. Procedure

The data was collected in three independent cross-sectional surveys technically conducted by the established German marketing and social research institute forsa via computer-assisted telephone interviews in sociodemographic representative samples between 2016 and 2018—for details see [30,31,32]. All study participants including the parents/caregivers of participating children gave informed consent prior to inclusion. They could withdraw from the study at any time without reason. All procedures performed in this study were in accordance with the ethical standards of the institutional and national research committee, with the 1964 Helsinki declaration as well as its later amendments or comparable ethical standards.

2.2. Participants

From the original three surveys, only data a) of 12- to 17-year old participants who b) completed the IGDS without omissions and c) reported playing any digital games at least once a week were included in the present study. This resulted in 8.5%, 22.4%, and 21.6% excluded cases [in 2016, 2017, 2018 respectively]. Proportions of excluded 12- to 13- years old were (except for 2016) lower than of 14- to 17-years old [10.6% vs. 4.7% in 2016, 15.1% vs. 26.2% in 2017, 17.2% vs. 23.7% in 2018], so were proportions of excluded boys compared to girls [3.3% vs. 13.8% in 2016, 8.9% vs. 31.6% in 2017, 7.4% vs. 38.2% in 2018]. The main reason for being excluded was gaming less than once a week or not at all. This concerned primarily girls. Accordingly, proportions of non-gaming girls vs. boys were 24.8% vs. 3.6% in 2016, 45.0% vs. 20.7% in 2017, and 46.4% vs. 11.1% in 2018. Since failure to meet only one of the three criteria mentioned above led to exclusion from the analysis, the cases affected can be understood as missing at random, and their exclusion represented only a negligible bias in the samples.
Data of the 2016, 2017, and 2018 samples were analyzed separately and not combined to take advantage of three independent replication trials. This makes it more likely to identify psychometric weaknesses that would go undetected in one joint large sample. The sample size was comparable in all surveys: 2016 (N = 762), 2017 (N = 777), and 2018 (N = 784). Age groups were based on the German social code (“Sozialgesetzbuch”) which defines children by law as younger than 14 years and adolescents as younger than 18 years.

2.3. Measures

Time spent with digital media was measured by asking for the “average time of playing any digital game on weekends (in minutes)” in the 2016 sample, and by asking for “the daily time of using any social media” in the 2017 sample in a multiple choice format (<60 min/60–119 min/120–179/180–239 min/>240 min). This format is viewed as more reliable by marketing research practitioners than asking for exact times in minutes. Psychopathology was screened in the 2018 sample by the established Strength and Difficulties Questionnaire (SDQ) by Goodman (https://www.sdqinfo.com/) with German norms [33]. Psychometric studies on German speaking children and adolescent samples reported mixed findings on internal scale consistencies [33,34,35,36]. These will be considered in the analyses and in the discussion section.

2.4. Statistical Analyses

For assessing psychometric properties, item analyses and internal consistencies were performed based on the “classical test theory”. Dimensionality of the IGDS was examined by confirmatory factor analyses (CFAs), item analyses, and principal component analyses (PCA). CFA fit indices were reported in comparison to the following recommended criteria: χ2/df ratio < 5, root mean square error of approximation (RMSEA) < 0.08, standardized root mean squared residual (SRMR) < 0.08, Tucker–Lewis Index (TLI) ≥ 0.95, comparative fit index (CFI) ≥ 0.95 [37]. Generalized least squares (GLS) were chosen as the parameter estimation method since no assumptions of multivariate normality among the observed variables were made. Following the “known group”-paradigm of convergent validation, diagnostic scoring “0–2 (normal)” on the IGDS were compared with those scoring “3–4 (at-risk gamers)” and “5+ (pathological gamers)” by analyses of variance in metric data (ANOVA) and by non-parametric U-tests in ordinal data. Because of sample size restrictions and resulting slanted cell frequencies in group comparisons, ANOVAs were preceded by non-parametric H tests, and significance was only assumed when both tests were significant.
All effect sizes (ES) are reported in Cohen’s d and η2. Considered conventions for d were: d > 0.20 = clinically relevant (small ES); 0.50 ≤ d < 0.80 medium ES, d ≥ 0.80 large ES [38]. Considered conventions for η2 are: η2 > 0.01 = clinically relevant (small); η2 ≥ 0.10 medium, η2 ≥ 0.25 large. ES were computed via Psychometrica software (https://www.psychometrica.de/) and CFAs via JASP 0.14.1.0 which is based on procedures written in the statistical programming language R (jasp-stats.org). All other computations were realized by IBM SPSS Statistics version 22 (https://www.ibm.com/de-de/products/spss-statistics).

3. Results

3.1. Sample Description

Table 1 reports the sociodemographic data of the three samples, divided by age groups and sex. About 95% of the sample attended school at the time of the interview. All samples of frequent gamers (at least once a week) consisted of a higher proportion of boys (64%) compared to girls (36%).
The distribution of IGDS scores is given in Table 2 together with the assessment outcomes. In the sample of 2016 pathological vs. at-risk gaming prevalence was 12 vs. 19%, 2017 it was 4 vs. 12%, and 2018 it was 3 vs. 11%. Prevalence of at-risk and pathological gaming was higher in boys than in girls. The total Cronbach’s alpha coefficients were 0.784 in 2016, 0.724 in 2017, and 0.514 in 2018. These are shown in Table 2 together with group-specific values. Over all samples, girls showed a mean alpha of 0.682, boys of 0.638, children (12–13 years) of 0.652 and adolescents (12–17 years) of 0.668.
Table A2 in the Appendix A shows the distribution of validation measures for time spent with digital media across the three samples (with means of typical weekend gaming time in minutes and medians of any daily social media use) and the mean SDQ total difficulties score. Moreover, the distributions of the SDQ subscale scores (Table A3) and alpha values (Table A4) are presented in the Appendix A. Due to non-satisfactory consistency values, results on the SDQ subscales ‘conduct problems’ and ‘peer relationship problems’ are reported but not interpreted. The same applies to the total difficulties scale as it is a linear combination of all four subscales and its good alpha values could be attributed merely to the scale length (20 items instead of 5), cp. [39].

3.2. Item Analysis and Dimensionality

Table 3 summarizes the results of confirmatory factor analyses (CFA) that were conducted to test the assumed IGDS unidimensionality. Moreover, classical item analyses were additionally conducted as PCAs, whereby the number of extractable components was restricted to k = 1. In all of the three samples the IGDS items did not clearly form one dimension. As depicted in Table 3, only RMSEA and χ2/df were clearly in favor of unidimensionality, items 4, 5, and 8 showed weak loadings indicating heterogeneity in item contents. According to Table 4, item 5 (escape, avoid thinking about disturbing things) yielded unacceptable values for item-total correlation (rit) and factor loadings (a) in all three samples. Item 8 (displacement, loss of interest) showed unsatisfactory rit or low α- values in the 2018 sample. However, the consistency of the scale was not significantly improved by omitting these items: the highest increase would have been from α = 0.563 to α = 0.586 in the 2018 sample. Inspection, overall component loadings were similar, but item difficulties and total item correlations varied across samples, again indicating some heterogeneity within components.

3.3. “Known-Groups” Analyses

3.3.1. Time Spent with Digital Media

Table 5 gives information about the average time of internet gaming and social media use. Differentiation between age groups was not useful, although desirable, due to the low cell frequencies in the gaming groups. On inspection, the gaming groups could be separated by the time spent with digital media: This was confirmed by significant overall test results with small ES (see Table 6). Planned comparisons (Helmert contrasts) of “normal vs. at-risk/pathological” revealed almost medium ES for internet gaming, and U tests showed small but clinically relevant ES for social media use. “At-risk vs. pathological” comparisons were non-significant and—with the exception of girls’ social media use―had small ES indicating a major overlap between these gaming groups in their temporal digital media use.

3.3.2. Emotional and Behavioral Difficulties

The distributions of the SDQ subscale scores of the 2018 sample, depending on IGDS gaming groups, are presented in Table 7. Again, differentiation by age groups is not reasonable due to the small cell sizes. Gaming groups could be separated according to the severity of emotional symptoms and hyperactivity/inattention, as indicated by the group means. Overall, ANOVA results showed significantly small ES (Table 8). The comparisons of “normal vs. at-risk/pathological” revealed almost medium ES for emotional symptoms and hyperactivity/inattention. The outcome for girls’ hyperactivity/inattention was not significant between the gaming groups but had an almost medium ES. In the “at-risk vs. pathological” comparisons regarding emotional problems and hyperactivity, 3 out of 6 were significant, two of them in boys, indicating a possible distinction but also overlap between the two gaming groups.
Table A5 in the Appendix A shows the distribution of emotional and behavioral problems (assessed by SDQ) depending on the IGDS categories. Approximately one-third of at-risk boys simultaneously reported borderline or clinically severe emotional problems and hyperactivity/inattention. The same is true for two fifths of the girls classified as at-risk according to the German standards of Becker et al. [33]. The results of Table A5 in the Appendix A should be considered with the caveat of small sample sizes.

4. Discussion

The current study aimed to increase the knowledge on psychometric properties and screening capabilities of a suitable IGD instrument for children and adolescents. Young gamers are especially vulnerable to develop at-risk or pathological gaming patterns leading to the need for a valid and easy applicable screening tool. The IGDS by Lemmens et al. [19] is one of the few screening instruments to have been initially validated in adolescents and based on the DSM-5 criteria. It showed good psychometric properties in prior research.

4.1. Psychometric Properties

The presented results did not indicate scale consistencies in favor of either sex or age groups. Lemmens et al. [19] reported a large Cronbach’s alpha of 0.93 and unidimensionality of the IGDS. These values, however, were not replicated in our samples where alphas varied from 0.56 (sample 3) via 0.72 (sample 2) to 0.78 (sample 1) with the lower value being regarded as insufficient. It has to be noted that the age range of the original study was 13 to 40 years and, thus, included children, adolescents, and adults. Values comparable to the ones of our sample 1 and 2 were described by Koning et al. (0.74 and 0.77) [40], Van den Eijnden et al. (0.73 and 0.76) [41], and Peters et al. (0.74 and 0.77) [42] based on longitudinal studies with adolescents.
IGDS items 8 (displacement) und especially 5 (escape) did not add to scale consistencies and omitting these items did not improve scale consistencies. From a test theoretical point of view, Schmitt [43] argues that a low Cronbach’s alpha does not seriously attenuate validity, but can still deliver useful information. Internal consistency is necessary, but not sufficient for validity. Moreover, alpha reflects not only scale property but also sample attributes [39]. Looking at item difficulties in Table 4, most IGDS items in sample 2018 show less agreement (fewer “yes” responses) than in the other samples, the exception is item 5 (escape). It may be an attribute of the 2018 sample of answering reluctantly. In 2018 there was an increased interest and a higher concern regarding potential risks of digital gaming in German media and school-based prevention programs. This might have fostered a tendency to downplay gaming behavior in order to answer as socially desirable and to appear “normal” to the interviewer. Thus, this contextual influence may have contributed to the weaker psychometric results in the 2018 sample compared to the other two. In the same year Jeong et al. [44] published a study showing a significant discrepancy between self-measurements and clinically verified IGD diagnoses among adolescents with a false-negative rate of 44%.
The assumption of unidimensionality was further challenged by weak CFA- and PCA-loadings of items 5 (escape) and 8 (displacement). As reported in the introduction, the criterion escape was considered by experts to have little diagnostic value [26]. The literature finding of Besser et al. [29] that the ‘escape’-item made up a factor of its own when investigating adolescents—as opposed to adult samples—was supported in all three samples of the current study. The authors commented: “Considering the young age of the participants (…), the use of the internet for mood modification might be an age-sensitive effect that should be considered in further studies (p. 292)” [29]. For reasons of comparability, the original IGDS items should be retained for research purposes but we suggest to reword the item for the DSM-5 criterion escape in future IGD measures.
Interestingly, earlier work by the authors of this study provided initial evidence that to describe gaming disorder in children and adolescents according to the new ICD-11 criteria, the use of two factors is superior to the use of a single factor. Here, one factor reflects the cognitive-behavioral gaming symptoms and the other factor reflects impending or manifest consequences due to gaming behavior [45,46]. In contrast, an ICD-11 screening tool for adults covering four items found one underlying factor only [47]. Yet, a two-factorial solution is in line with the biaxial model of addiction where an addictive behavior is defined as pathological only when both specific symptoms and adverse outcomes occur [48]. The IGDS includes functional impairments by two out of the nine symptoms (according to DSM-5 criteria ‘problems’ and ‘conflict’), weakly loading on the main IGDS factor in all three samples. Since the scale evaluation follows a polythetic principle with every item being weighted equally, an IGD can be assumed without any impairment symptoms. Esposito et al. [49] argue for a careful consideration of monothetic or different polythetic questionnaire evaluations.

4.2. Convergent Validity

According to the “known group”-paradigm of convergent validation, the groups of normal, at-risk and pathological gamers were separable by ANOVAs, as well as by non-parametric H tests and U tests. In at-risk and pathological gamers compared to normal gamers, “typical weekend gaming time” was longer with clinically relevant effect sizes (2016 sample), and percentage of “4+ hours daily social media use” was larger (2017 sample).
Emotional and behavioral assessment via SDQ German norms [33] revealed emotional symptoms to be much more pronounced in at-risk/pathological gamers than in normal gamers (2018 sample) with mainly medium effect sizes. The computed ES correspond to correlation coefficients reported by Lemmens et al. [19] and Wartberg et al. [26] varying between r = 0.227 (d = 0.466) and r = 0.311 (d = 0.654). By structural equation modeling in a cross-lagged panel design, Wartberg et al. [26] found the following variables to predict IGD in N = 955 children aged 12–14 years after one year: male sex, IGD at initial assessment, as well as hyperactivity/inattention and self-esteem problems. Accordingly, it can be assumed that “at-risk” adolescents could develop a manifest pathological gaming disorder if clinically relevant hyperactivity/inattention and/or emotional symptoms are present, and no clinical intervention takes place.
Exploratory testing for group differences between at-risk gamers and pathological gamers yielded mixed results. This is conclusive because “at-risk” gamers form a heterogeneous group. However, an increased psychopathologic burden was found in this group which is worth further clinical evaluation. Thus, for clinical purposes, investigating frequent gamers by applying a cut-off score of 5+ only does not seem to be satisfying. Even an IGDS score of 3+ seems to indicate an elevated risk of co-occurring emotional and behavioral problems. However, it has to be kept in mind, that by reducing the IGDS cut-off, the chance of detecting at-risk gamers will be increased while the risk of misclassifying regular gamers as problematic will be elevated. Accordingly, Colder Carras and Kardefelt-Winter [48] argue for a careful consideration of both addiction-related symptoms and gaming-related impairments [48]. In their large-scale investigation of 7865 adolescent European gamers, 2.2% of the sample reported symptoms and impairments while 30.9% of the adolescents would have been misclassified based on their personal problems only. Whereas 23.6% of “concerned” gamers indicated only a few addiction symptoms but a high level of impairments, 7.3% of “engaged” gamers stated many addiction symptoms but only a few impairments. While the first group might resemble adolescents with higher emotional and behavioral problems with accompanied non-addictive gaming, the latter group might correspond most likely to the group of ICD-11 hazardous gamers. A distinction between these two groups is not possible by the IGDS.

4.3. Limitations

In the present study, comparisons between the gaming groups were limited due to low disorder prevalence, although gender-, sex-, and age-sensitive research on internet gaming is desirable. Future studies should address this issue by an oversampling of pathological and at-risk gamers. The present findings are based on cross-sectional survey data. Thus, no conclusions can be drawn about symptom stability and retest reliability. A further shortcoming of the current study is the missing criterion validity with objective markers such as logged usage times. Furthermore, to the best of our knowledge, the IGDS has not yet been validated against clinical evaluation in adolescents which is the gold standard when pursuing diagnostic purposes. Therefore, the IGDS is suitable for epidemiological research but not adequate for individual assessment. Finally, IGDS results cannot be easily transferred to evaluate a gaming disorder according to the ICD-11 criteria and there is no clear equivalent to ICD-11 hazardous gaming. Thus, studies comparing both diagnostic approaches would be of great interest.

5. Conclusions

The current study is the first to investigate the psychometric properties and the criterion validity of an established IGD screener in three large independent representative samples of children and adolescents. Since IGD in this young target group is of high clinical relevance, a suitable and easy to administer screening tool is urgently needed. Based on the present findings, the use of the IGDS by Lemmens et al. [17] in children and adolescents as a valid screening tool on a populational level could be supported. However, the assumption of unidimensionality was challenged. Normal gamers could be reliably differentiated from at-risk and pathological gamers. However, the heterogeneous group of at-risk gamers could not be further evaluated due to scale limitations. Since the IGDS is a polythetic tool with equal weight of each item, the presence of gaming-associated impairments is not necessary for IGD classification. Along with the missing equivalent of hazardous gaming criteria, a direct comparison of the gaming groups with the new ICD-11 definitions is limited.

Author Contributions

Conceptualization, K.P. and R.T.; methodology, K.P. and P.-M.S.; software, P.-M.S.; validation, K.P. and P.-M.S.; formal analysis, P.-M.S.; investigation, K.P.; resources, K.P.; data curation, K.P.; writing—original draft preparation, K.P. and P.-M.S.; writing—review and editing, K.P., P.-M.S., and R.T.; supervision, R.T.; project administration, K.P.; funding acquisition, R.T. All authors have read and agreed to the published version of the manuscript.

Funding

This work was financially supported by the German statutory health insurance DAK Gesundheit.

Institutional Review Board Statement

The study was conducted according to the guidelines of the Declaration of Helsinki 1975, revised in 2013. After consultation with the local ethics committee, ethical review and approval were waived for this study. The study was non-interventional and without impact on the physical and psychological integrity of participants, as participants had no patient status in relation to the authors, and as authors received participants’ data in anonymized form (please refer to DFG guidelines for German researchers, German Research Foundation - FAQ: Humanities and Social Sciences). Participants were enrolled based on anonymous panel data. Neither the interviewer, nor the researchers had any access to personal data that would have allowed identification. Thus, data collection and data transfer was completely anonymous and identification cannot be reconstructed by any source.

Informed Consent Statement

All study participants including the parents/caregivers of participating children gave informed consent prior to inclusion. They could withdraw from the interview at any time without reason.

Data Availability Statement

The data presented in this study are available from the corresponding author upon reasonable request.

Acknowledgments

The authors would like to thank all study participants and the German market and opinion research company forsa for the excellent data collection.

Conflicts of Interest

The authors declare no conflict of interest. DAK Gesundheit had no role in the design of the study, collection, analyses, or interpretation of data, in the writing of the manuscript, or in the decision to publish the results.

Appendix A

Table A1. Brief overview of diagnostic criteria for the internet gaming disorder (IGD).
Table A1. Brief overview of diagnostic criteria for the internet gaming disorder (IGD).
LabelCriterion
A.Mental preoccupation with gaming
B.Withdrawal symptoms
C.Tolerance/increase of dosage (gaming time)
D.Failures to gain gaming control
E.Loss of previous interests or prior hobbies
F.Continuation of gaming despite insight into adverse consequences
G.Lying to significant others in respect of factual gaming
H.Gaming in order to regulate negative moods (‘escape’)
I.Elevated risk of losing an important social relationship (job/education)
Notes. For assuming an IGD, a person’s gaming behavior must have matched at least 5 out of these 9 criteria in the past 12 months. See details in APA [11].
Table A2. Distribution of validation measures across the three samples a.
Table A2. Distribution of validation measures across the three samples a.
Age GroupSexb Typical Weekend
Gaming Time (Minutes)
c Daily Social Media Useb d SDQ Total
Difficulties Score
201620172018
12–13Girls120.11(59.71)120–179 min8.10(4.47)
Boys178.40(105.53)120–179 min8.14(4.47)
14–17Girls147.97(102.62)180–239 min9.47(4.70)
Boys223.70(120.50)120–179 min7.47(4.28)
Total 174.64(110.83)120–179 min8.16(4.52)
Notes. a Sample 2016: N = 762, sample 2017: N = 777, sample 2018: N = 784; b group mean (standard deviation); c median category, maximum category would be 240+ minutes; d distribution of Strength and Difficulties Questionnaire (SDQ) subscales are given in Table 7 (main text); maximum score would be 40.
Table A3. Distribution of SDQ validation measure in the 2018 sample (N = 784).
Table A3. Distribution of SDQ validation measure in the 2018 sample (N = 784).
Age GroupSexa Emotional SymptomsConduct ProblemsHyperactivity/InattentionPeer Relationship Problems
12–13Girls2.01(1.87)1.30(1.26)3.30(2.36)2.24(1.69)
Boys1.81(1.71)1.33(1.39)3.33(2.28)1.91(1.59)
14–17Girls3.41(2.40)1.15(1.15)2.67(1.96)2.24(1.69)
Boys1.70(1.71)1.22(1.12)2.64(1.99)1.91(1.59)
Total 2.15(2.03)1.24(1.20)2.88(2.12)1.88(1.57)
Notes. a Group mean (standard deviation).
Table A4. Internal consistencies (Cronbach’s alpha) of SDQ subscales in the 2018 sample.
Table A4. Internal consistencies (Cronbach’s alpha) of SDQ subscales in the 2018 sample.
SexEmotional Symptomsa Conduct ProblemsHyperactivity/Inattentionb Peer Relationship Problemsc Total
Difficulties Score
Girls0.7120.2910.6420.4690.709
Boys0.5460.3120.6270.4730.692
Total0.6560.3040.6320.4700.701
Notes. a Alpha not acceptable, b not satisfactory. c Satisfactory alphas here are at least partly due to scale length (20 items), cp. [39].
Table A5. Percentages of children and adolescents screened with “borderline/clinical” SDQ results according to gaming group classification in the 2018 sample.
Table A5. Percentages of children and adolescents screened with “borderline/clinical” SDQ results according to gaming group classification in the 2018 sample.
SexIGDS Scorea Emotional Symptomsb Conduct ProblemsHyperactivity/Inattentionc Peer relation-Ship Problems
%n%n%n%n
GirlsNormal14.6(38)8.8(23)11.9(31)13.8(36)
At-risk38.1(8)28.6(6)23.6(5)33.3(7)
Pathologic66.7(2)33.3(1)33.3(1)33.3(1)
BoysNormal10.5(43)3.6(15)7.5(31)15.8(65)
At-risk32.8(22)11.9(8)28.4(19)25.4(17)
Pathologic57.1(12)19.0(4)33.3(7)42.9(9)
TotalNormal12.1(81)5.7(38)9.2(62)15.0(101)
At-risk34.1(30)15.9(14)27.3(24)27.3(24)
Pathologic58.3(14)20.8(5)33.3(8)41.7(10)
Notes. a Percentage (absolute frequency). Internal consistency b not acceptable or c not satisfactory. Age- and sex-sensitive SDQ cut-off scores []; N = 784, nboys = 499, ngirls = 285.

References

  1. WARC. Coronavirus Research: Multi-Market Research Wave 4. WARC, 2020. Available online: http://origin.warc.com/content/paywall/article/Warc-Research/GWI_Coronavirus_Research_Multimarket_research_wave_4/132717 (accessed on 20 June 2020).
  2. statista.com. Most Popular Apple App Store Categories in June 2020, by Share of Available Apps. Available online: https://www.statista.com/statistics/270291/popular-categories-in-the-app-store/ (accessed on 20 June 2020).
  3. statista.com. Online Games. Available online: https://www.statista.com/outlook/212/100/online-games/worldwide (accessed on 20 June 2020).
  4. Rehbein, F.; Psych, G.; Kleimann, M.; Mediasci, G.; Mößle, T. Prevalence and risk factors of video game dependency in adolescence: Results of a German nationwide survey. Cyberpsychol. Behav. Soc. Netw. 2010, 13, 269–277. [Google Scholar] [CrossRef]
  5. Veissière, S.P.L.; Stendel, M. Hypernatural monitoring: A social rehearsal account of smartphone addiction. Front. Psychol. 2018, 9. [Google Scholar] [CrossRef]
  6. Paschke, K.; Austermann, M.I.; Thomasius, R. Adolescent gaming and social media usage before and during the COVID-19 pandemic. Manuscript accepted for publication. Sucht 2021, 67. [Google Scholar] [CrossRef]
  7. Konrad, K.; Firk, C.; Uhlhaas, P.J. Brain development during adolescence: Neuroscientific insights into this developmental period. Dtsch. Arztebl. Int. 2013, 110, 425. [Google Scholar] [CrossRef]
  8. Arain, M.; Haque, M.; Johal, L.; Mathur, P.; Nel, W.; Rais, A.; Sandhu, R.; Sharma, S. Maturation of the adolescent brain. Neuropsychiatr. Dis. Treat. 2013, 9, 449–461. [Google Scholar] [CrossRef]
  9. Yuan, K.; Qin, W.; Yu, D.; Bi, Y.; Xing, L.; Jin, C.; Tian, J. Core brain networks interactions and cognitive control in internet gaming disorder individuals in late adolescence/early adulthood. Brain. Struct. Funct. 2016, 221, 1427–1442. [Google Scholar] [CrossRef]
  10. Sugaya, N.; Shirasaka, T.; Takahashi, K.; Kanda, H. Bio-psychosocial factors of children and adolescents with internet gaming disorder: A systematic review. BioPsychoSocial Med. 2019, 13, 3. [Google Scholar] [CrossRef]
  11. APA American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders: DSM-5; American Psychiatric Association: Washington, DC, USA, 2013. [Google Scholar]
  12. Paulus, F.W.; Ohmann, S.; Von Gontard, A.; Popow, C. Internet gaming disorder in children and adolescents: A systematic review. Dev. Med. Child. Neurol. 2018, 60, 645–659. [Google Scholar] [CrossRef]
  13. Fuster, H.; Carbonell, X.; Pontes, H.M.; Griffiths, M.D. Spanish validation of the Internet Gaming Disorder (IGD-20) Test. Comp. Hum. Behav. 2016, 56, 215–224. [Google Scholar] [CrossRef]
  14. Pontes, H.M.; Király, O.; Demetrovics, Z.; Griffiths, M.D. The conceptualisation and measurement of DSM-5 Internet Gaming Disorder: The development of the IGD-20 Test. PLoS ONE 2014, 9, e110137. [Google Scholar] [CrossRef]
  15. Milani, L.; La Torre, G.; Fiore, M.; Grumi, S.; Gentile, D.A.; Ferrante, M.; Di Blasio, P. Internet gaming addiction in adolescence: Risk factors and maladjustment correlates. Int. J. Ment. Health Addict. 2018, 16, 888–904. [Google Scholar] [CrossRef]
  16. Torres-Rodríguez, A.; Griffiths, M.D.; Carbonell, X.; Oberst, U. Internet gaming disorder in adolescence: Psychological characteristics of a clinical sample. J. Behav. Addict. 2018, 7, 707–718. [Google Scholar] [CrossRef]
  17. World Health Organization. International Classification of Diseases for Mortality and Morbidity Statistics. Available online: https://icd.who.int/browse11/l-m/en (accessed on 13 January 2020).
  18. Király, O.; Tóth, D.; Urbán, R.; Demetrovics, Z.; Maraz, A. Intense video gaming is not essentially problematic. Psychol. Addict. Behav. 2017, 31, 807–817. [Google Scholar] [CrossRef]
  19. Lemmens, J.S.; Valkenburg, P.M.; Gentile, D.A. The Internet Gaming Disorder Scale. Psychol. Assess. 2015, 27, 567–582. [Google Scholar] [CrossRef]
  20. Pontes, H.M.; Griffiths, M.D. Measuring DSM-5 Internet gaming disorder: Development and validation of a short psychometric scale. Comput. Hum. Behav. 2015, 45, 137–143. [Google Scholar] [CrossRef]
  21. Donati, M.A.; Sanson, F.; Mazzarese, M.; Primi, C. Assessing video game habits and pathological behaviour in children through a new scale: Psychometric properties of the Video-Gaming Scale—For Children (VGS-C). Psychology 2019, 10, 2190–2208. [Google Scholar] [CrossRef]
  22. Primi, C.; Donati, M.A.; Chiesi, F. Scala per la Misura Dell’uso dei Videogiochi negli Adolescenti [Video-Gaming Scale for Adolescents, VGS-A.]; Hogrefe: Florence, Italy, 2017. [Google Scholar]
  23. King, D.L.; Chamberlain, S.R.; Carragher, N.; Billieux, J.; Stein, D.; Delfabbro, P.H. Screening and assessment tools for gaming disorder: A comprehensive systematic review. Clin. Psychol. Rev. 2020, 77, 101831. [Google Scholar] [CrossRef]
  24. Schivinski, B.; Brzozowska-Woś, M.; Buchanan, E.M.; Griffiths, M.D.; Pontes, H.M. Psychometric assessment of the internet gaming disorder diagnostic criteria: An item response theory study. Addict. Behav. Rep. 2018, 8, 176–184. [Google Scholar] [CrossRef]
  25. Wartberg, L.; Kriston, L.; Zieglmeier, M.; Lincoln, T.; Kammerl, R. A longitudinal study on psychosocial causes and consequences of Internet gaming disorder in adolescence. Psychol. Med. 2019, 49, 287–294. [Google Scholar] [CrossRef]
  26. Wartberg, L.; Kriston, L.; Thomasius, R. Internet gaming disorder and problematic social media use in a representative sample of German adolescents: Prevalence estimates, comorbid depressive symptoms and related psychosocial aspects. Comp. Hum. Behav. 2020, 103, 31–36. [Google Scholar] [CrossRef]
  27. Castro–Calvo, J.; King, D.L.; Stein, D.J.; Brand, M.; Carmi, L.; Chamberlain, S.R.; Billieux, J. Expert appraisal of criteria for assessing gaming disorder: An international Delphi study. Addiction 2021. [Google Scholar] [CrossRef]
  28. Meerkerk, G.J.; Van Den Eijnden, R.; Vermulst, A.A.; Garretsen, H.F.L. The Compulsive Internet Use Scale (CIUS): Some psychometric properties. CyberPsychology 2009, 12, 1–6. [Google Scholar] [CrossRef]
  29. Besser, B.; Loerbroks, L.; Bischof, G.; Bischof, A.; Rumpf, H.-J. Performance of the DSM-5-based criteria for Internet addiction: A factor analytical examination of three samples. J. Behav. Addict. 2019, 8, 288–294. [Google Scholar] [CrossRef]
  30. Wartberg, L.; Kriston, L.; Thomasius, R. The prevalence and psychosocial correlates of internet gaming disorder—analysis in a nationally representative sample of 12-to 25-year-olds. Dtsch. Arztebl. Int. 2017, 114, 419–424. [Google Scholar] [CrossRef]
  31. Wartberg, L.; Kriston, L.; Thomasius, R. Depressive Symptoms in Adolescents. Dtsch. Arztebl. Int. 2018, 115, 549–555. [Google Scholar] [CrossRef]
  32. Paschke, K.; Laurenz, L.; Thomasius, R. Chronic Sleep Reduction in Childhood and Adolescence. Dtsch. Arztebl. Int. 2020, 117, 661–667. [Google Scholar] [CrossRef]
  33. Becker, A.; Wang, B.; Kunze, B.; BELLA study group, T. Normative data of the self-report version of the German Strengths and Difficulties Questionnaire in an epidemiological setting. Z. Kinder Jugendpsychiatr. Psychother. 2018, 46, 523–533. [Google Scholar] [CrossRef]
  34. Altendorfer-Kling, U.; Ardelt-Gattinger, E.; Thun-Hohenstein, L. [The self-report version of the Strengths and Difficulties Questionnaire in an Austrian field sample. In German]. Z. Kinder Jugendpsychiatr. Psychother. 2007, 35, 265–271. [Google Scholar] [CrossRef]
  35. Lohbeck, A.; Schultheiss, J.; Petermann, F.; Petermann, U. The German self-report version of the strengths and difficulties questionnaire (SDQ-Deu-S): Psychometric properties, factor structure, and critical values. Diagnostica 2015, 61, 222–235. [Google Scholar] [CrossRef]
  36. Keller, F.; Langmeyer, A.N. An item response theory analysis of the Strengths and Difficulties Questionnaire (SDQ). Eur. J. Psychol. Assess. 2019, 35, 266–279. [Google Scholar] [CrossRef]
  37. Hooper, D.; Coughlan, J.; Mullen, M.R. Structural equation modelling: Guidelines for determining model fit. Electr. J. Bus. Res. Meth. 2008, 6, 53–60. [Google Scholar] [CrossRef]
  38. Cohen, J. Statistical Power Analysis for the Behavioral Sciences, 2nd ed.; Routledge/Earlbaum: New York, NY, USA, 1988. [Google Scholar]
  39. Taber, K.S. The use of Cronbach’s alpha when developing and reporting research instruments in science education. Res. Sci. Educ. 2018, 48, 1273–1296. [Google Scholar] [CrossRef]
  40. Koning, I.M.; Peeters, M.; Finkenauer, C.; Van Den Eijnden, R.J. Bidirectional effects of Internet-specific parenting practices and compulsive social media and Internet game use. J. Behav. Addict. 2018, 7, 624–632. [Google Scholar] [CrossRef]
  41. Van Den Eijnden, R.; Koning, I.; Doornwaard, S.; Van Gurp, F.; Ter Bogt, T. The impact of heavy and disordered use of games and social media on adolescents’ psychological, social, and school functioning. J. Behav. Addict. 2018, 7, 697–706. [Google Scholar] [CrossRef]
  42. Peeters, M.; Koning, I.; van den Eijnden, R. Predicting Internet gaming disorder symptoms in young adolescents: A one-year follow-up study. Comput. Hum. Behav. 2018, 80, 255–261. [Google Scholar] [CrossRef]
  43. Schmitt, N. Uses and abuses of coefficient alpha. Psychol. Assess. 1996, 8, 350–353. [Google Scholar] [CrossRef]
  44. Jeong, H.; Yim, H.W.; Lee, S.-Y.; Lee, H.K.; Potenza, M.N.; Choi, J.-S. Discordance between self-report and clinical diagnosis of Internet gaming disorder in adolescents. Sci. Rep. 2018, 8, 10084. [Google Scholar] [CrossRef]
  45. Paschke, K.; Austermann, M.I.; Thomasius, R. Assessing ICD-11 gaming disorder in adolescent gamers: Development and validation of the Gaming Disorder Scale for Adolescents (GADIS-A). J. Clin. Med. 2020, 9, 993. [Google Scholar] [CrossRef]
  46. Paschke, K.; Austermann, M.I.; Thomasius, R. Assessing ICD-11 gaming disorder in adolescent gamers by parental ratings: Development and validation of the Gaming Disorder Scale for Parents (GADIS-P). J. Behav. Addict. 2021. [Google Scholar] [CrossRef]
  47. Pontes, H.M.; Schivinski, B.; Sindermann, C.; Li, M.; Becker, B.; Zhou, M.; Montag, C. Measurement and conceptualization of gaming disorder according to the World Health Organization framework: The development of the Gaming Disorder Test. Int. J. Ment. Health. Add. 2019, 1–21. [Google Scholar] [CrossRef]
  48. Colder Carras, M.; Kardefelt-Winther, D. When addiction symptoms and life problems diverge: A latent class analysis of problematic gaming in a representative multinational sample of European adolescents. Eur. Child. Adolesc. Psychiatry 2018, 27, 513–525. [Google Scholar] [CrossRef]
  49. Esposito, M.R.; Serra, N.; Guillari, A.; Simeone, S.; Sarracino, F.; Continisio, G.I.; Rea, T. An investigation into video game addiction in pre-adolescents and adolescents: A cross-sectional study. Medicina (Kaunas) 2020, 56, 221. [Google Scholar] [CrossRef]
Table 1. Sociodemographic data of the three samples employed in this study.
Table 1. Sociodemographic data of the three samples employed in this study.
SampleAgeSexSchoolJob-TrainingOther aTotal
201612–13Girls133------133
Boys150------150
14–17Girls198177222
Boys236183257
Total
%
717
94.1
35
4.6
10
1.3
762
100
201712–13Girls129------129
Boys163------163
14–17Girls17585222
Boys271215257
Total
%
738
95.0
29
3.4
10
1.3
777
100
201812–13Girls108------108
Boys162------162
14–17Girls162114222
Boys303295257
Total
%
735
93.8
40
5.1
9
1.1
784
100
Notes. a Other is study, military/social service, job-seeking, or job.
Table 2. Internet Gaming Disorder Scale (IGDS) scores, assessments, and consistency coefficients across the three samples employed a.
Table 2. Internet Gaming Disorder Scale (IGDS) scores, assessments, and consistency coefficients across the three samples employed a.
SampleAge GroupSexb Sum ScoreCronbach’s Alphac Portion of at-Risk Gamersc Portion of Pathologic Gamers
201612–13Girls1.56(1.84)0.7547.1(20)3.9(11)
Boys2.71(2.17)0.70315.9(45)10.2(29)
14–17Girls1.01(1.58)0.7843.8(18)2.5(12)
Boys2.38(2.20)0.77012.7(61)8.8(42)
Total 1.90(2.08)0.77818.9(144)12.3(94)
201712–13Girls0.71(1.34)0.7562.7(8)1.0(3)
Boys1.60(1.65)0.6139.6(28)3.1(9)
14–17Girls0.61(1.05)0.6001.9(9)0.4(2)
Boys1.60(1.71)0.6469.9(48)4.1(20)
Total 1.21(1.56)0.72412.0(93)4.4(34)
201812–13Girls0.75(1.10)0.5143.3(9)0.4(1)
Boys1.45(1.56)0.57010.4(28)2.6(7)
14–17Girls0.74(1.15)0.6822.3(12)0.4(2)
Boys1.21(1.37)0.5287.6(39)2.7(14)
Total 1.09(1.36)0.56311.2(88)3.1(24)
Notes. a based on N = 762 for 2016, N = 777 for 2017, N = 784 for 2018; b group mean (standard deviation); c percentages (frequencies).
Table 3. Results of confirmatory factor analyses of the Internet Gaming Disorder Scale IGDS in three different samples plus fit indices.
Table 3. Results of confirmatory factor analyses of the Internet Gaming Disorder Scale IGDS in three different samples plus fit indices.
Index201620172018Cut-off
χ2/df82.688/27 = 3.063
p < 0.001
50.563/27 =1.872
p = 0.004
42.656/27 = 1.580
p = 0.028
<5
CFI0.7680.8850.903≥0.95
RMSEA0.0600.0340.027<0.08
TLI0.7680.8470.871≥0.95
ItemsLoadings
1. Preoccupation0.2330.2290.194
2. Tolerance0.2800.2050.184
3. Withdrawal0.3030.1790.147
4. Persistence0.2150.1150.092
5. Escape0.0960.1590.092
6. Problems0.2310.1420.112
7. Deception0.1840.1110.102
8. Displacement0.1720.0990.035
9. Conflict0.1480.0550.071
Notes. CFI = comparative fit index, RMSEA = root mean square error of approximation, TLI = Tucker–Lewis-index (non-normed fit index). All item loadings p < 0.001.
Table 4. Item analyses of the Internet Gaming Disorder Scale IGDS in three different samples.
Table 4. Item analyses of the Internet Gaming Disorder Scale IGDS in three different samples.
Sample201620172018
Itemp (i)ritap (i)ritap (i)rita
1. Preoccupation19.00.4880.61719.50.4630.65413.40.4040.648
2. Tolerance27.80.4930.61316.60.4400.62312.90.3690.632
3. Withdrawal24.40.5720.67614.40.4000.59013.30.2950.537
4. Persistence18.20.4730.64214.00.2610.41412.40.2320.389
5. Escape26.90.2020.25526.40.2900.45127.80.1520.283
6. Problems24.80.4680.62712.10.3440.53413.50.2570.454
7. Deception17.50.4170.5288.80.3190.4947.50.2770.511
8. Displacement13.40.4670.5496.50.3230.4994.10.1440.272
9. Conflict9.00.4460.5952.20.2960.4714.20.2890.482
Notes. Item difficulty p (i); item-total correlation rit part-whole corrected; loadings a according to principal component analyses restricted to k = 1 component.
Table 5. Time spent with gaming (2016 sample, N = 762) and social media (2017 sample N = 777), depending on IGDS score.
Table 5. Time spent with gaming (2016 sample, N = 762) and social media (2017 sample N = 777), depending on IGDS score.
SexIGDS Scorea Typical Weekend
Gaming Time (Minutes)
b Daily Social Media Use 4+ Hours
GirlsNormal129.95(85.78)3.5(23)
At-risk162.24(104.38)4.3(4)
Pathological193.70(93.12)8.8(3)
BoysNormal184.02(103.47)14.2(92)
At-risk236.18(125.12)30.1(28)
Pathological237.89(130.70)41.2(14)
TotalNormal153.68(97.63)17.7(115)
At-risk216.67(124.03)34.4(32)
Pathological227.07(123.59)50.0(17)
Notes. a Group mean (standard deviation); b percentages (frequencies). Results of statistical significance tests are given in Table 6.
Table 6. Time spent with gaming (2016 sample) and social media (2017 sample): results of significance tests and effect sizes for Table 5.
Table 6. Time spent with gaming (2016 sample) and social media (2017 sample): results of significance tests and effect sizes for Table 5.
Typical weekend gaming time
Univariate analyses of varianceHelmert contrasts p; effect size d
dfFpη2Normal vs. at-risk/pathologicAt-risk vs. pathologic
2016Girls27.210.0010.039<0.0010.4490.1790.252
Boys210.67<0.0010.050<0.0010.4500.9230.143
Total232.77<0.0010.079<0.0010.5770.4810.084
Daily social media use 4+ hours
a Kruskal–Wallis H-testsb Mann–Whitney U-tests p; effect size d
dfFpη2Normal vs. at-risk/pathologicAt-risk vs. pathologic
2017Girls219.630.0010.4890.0020.2120.2740.536
Boys28.990.0110.2500.0070.2050.3730.177
Total231.22<0.0010.397<0.0010.2690.1490.240
Notes. a Kruskal–Wallis H-tests are exact. b Mann–Whitney U-tests are exact.
Table 7. Distribution of Strength and Difficulties Questionnaire (SDQ) subscales in the 2018 sample, depending on IGDS score.
Table 7. Distribution of Strength and Difficulties Questionnaire (SDQ) subscales in the 2018 sample, depending on IGDS score.
SexIGDS Scorea Emotional Problemsb Conduct ProblemsHyperactivity/Inattentionc Peer Relationship Problems
GirlsNormal2.73(2.18)1.15(1.11)2.82(2.12)1.88(1.54)
At-risk4.33(3.09)1.90(1.70)4.10(1.92)2.90(1.61)
Pathological6.00(2.65)1.67(2.08)2.00(3.46)2.33(2.31)
BoysNormal1.51(1.48)1.14(1.11)2.60(1.95)1.72(1.46)
At-risk2.42(2.18)1.84(1.47)3.84(2.26)2.27(1.18)
Pathological3.95(2.13)1.71(1.59)4.95(2.64)2.71(2.31)
TotalNormal1.99(1.88)1.14(1.11)2.68(2.02)1.78(1.49)
At-risk2.88(2.54)1.85(1.52)3.90(2.18)2.42(1.79)
Pathological4.21(2.25)1.71(1.60)4.58(2.84)2.67(2.26)
Notes. a Group mean (standard deviation). Alphas b not acceptable or c not satisfactory.
Table 8. SDQ subscales scores (2018 sample): results of significance tests between IGDS gaming groups, Helmert contrasts and effect sizes for Table 5.
Table 8. SDQ subscales scores (2018 sample): results of significance tests between IGDS gaming groups, Helmert contrasts and effect sizes for Table 5.
Univariate ANOVA ResultsHelmert Contrasts and Effect Sizes
Normal vs. at-Risk/PathologicalAt-Risk vs. Pathological
dfFpη2pdpd
Emotional problems
Girls27.790.0010.0520.0010.6100.2330.616
Boys229.42<0.0010.106<0.0010.595<0.0010.716
Total221.28<0.0010.052<0.0010.4800.0030.577
a Conduct problems
Girls24.290.0150.0300.0840.4390.7430.117
Boys211.57<0.0010.045<0.0010.4670.6820.078
Total216.08<0.0010.040<0.0010.4600.5960.091
Hyperactivity inattention
Girls23.800.0230.0260.7330.4660.1100.633
Boys222.43<0.0010.083<0.0010.6540.0280.438
Total221.87<0.0010.053<0.0010.5930.1500.253
b Peer relationship problems
Girls24.320.0140.0300.1320.5770.5510.256
Boys29.220.0010.028<0.0010.3520.2520.202
Total29.79<0.0010.024<0.0010.4040.4920.114
Notes. Alphas a not acceptable or b not satisfactory.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Back to TopTop